Overview

Dataset statistics

Number of variables14
Number of observations837
Missing cells2271
Missing cells (%)19.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory360.8 KiB
Average record size in memory441.4 B

Variable types

Numeric6
Text4
Boolean2
Categorical2

Alerts

countryCode is highly overall correlated with latitude and 2 other fieldsHigh correlation
latitude is highly overall correlated with countryCode and 1 other fieldsHigh correlation
longitude is highly overall correlated with countryCode and 1 other fieldsHigh correlation
timezone is highly overall correlated with countryCode and 2 other fieldsHigh correlation
dome is highly imbalanced (80.6%)Imbalance
countryCode is highly imbalanced (96.8%)Imbalance
capacity has 21 (2.5%) missing valuesMissing
grass has 511 (61.1%) missing valuesMissing
zip has 112 (13.4%) missing valuesMissing
timezone has 523 (62.5%) missing valuesMissing
latitude has 42 (5.0%) missing valuesMissing
longitude has 42 (5.0%) missing valuesMissing
elevation has 504 (60.2%) missing valuesMissing
constructionYear has 506 (60.5%) missing valuesMissing
id has unique valuesUnique
capacity has 46 (5.5%) zerosZeros

Reproduction

Analysis started2025-12-08 06:34:56.492975
Analysis finished2025-12-08 06:34:57.942439
Duration1.45 second
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct837
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4870.6213
Minimum36
Maximum11724
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2025-12-07T22:34:57.967445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile3607.8
Q13794
median4463
Q35925
95-th percentile6288.8
Maximum11724
Range11688
Interquartile range (IQR)2131

Descriptive statistics

Standard deviation1477.2522
Coefficient of variation (CV)0.30329852
Kurtosis4.020678
Mean4870.6213
Median Absolute Deviation (MAD)842
Skewness0.72205148
Sum4076710
Variance2182274.1
MonotonicityNot monotonic
2025-12-07T22:34:58.012493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59381
 
0.1%
39201
 
0.1%
60541
 
0.1%
47531
 
0.1%
60681
 
0.1%
57851
 
0.1%
60771
 
0.1%
44401
 
0.1%
38001
 
0.1%
60311
 
0.1%
Other values (827)827
98.8%
ValueCountFrequency (%)
361
0.1%
1501
0.1%
2181
0.1%
3471
0.1%
4531
0.1%
4771
0.1%
4991
0.1%
5381
0.1%
5871
0.1%
7161
0.1%
ValueCountFrequency (%)
117241
0.1%
117231
0.1%
117121
0.1%
115911
0.1%
115891
0.1%
115391
0.1%
114881
0.1%
108031
0.1%
104651
0.1%
104631
0.1%

name
Text

Distinct829
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size56.2 KiB
2025-12-07T22:34:58.149483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length55
Median length40
Mean length19.645161
Min length8

Characters and Unicode

Total characters16443
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique823 ?
Unique (%)98.3%

Sample

1st rowAl Whitehead Field at Greyhound Stadium
2nd rowFIU Stadium
3rd rowThomas A. Robinson National Stadium
4th rowLokken Stadium
5th rowGarrison Stadium
ValueCountFrequency (%)
stadium583
23.9%
field217
 
8.9%
memorial49
 
2.0%
at44
 
1.8%
complex21
 
0.9%
bowl18
 
0.7%
alumni16
 
0.7%
athletic15
 
0.6%
park13
 
0.5%
bank12
 
0.5%
Other values (1040)1449
59.5%
2025-12-07T22:34:58.322619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1600
 
9.7%
i1387
 
8.4%
a1319
 
8.0%
e1127
 
6.9%
t1048
 
6.4%
d1022
 
6.2%
m831
 
5.1%
l814
 
5.0%
u769
 
4.7%
S724
 
4.4%
Other values (54)5802
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)16443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1600
 
9.7%
i1387
 
8.4%
a1319
 
8.0%
e1127
 
6.9%
t1048
 
6.4%
d1022
 
6.2%
m831
 
5.1%
l814
 
5.0%
u769
 
4.7%
S724
 
4.4%
Other values (54)5802
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1600
 
9.7%
i1387
 
8.4%
a1319
 
8.0%
e1127
 
6.9%
t1048
 
6.4%
d1022
 
6.2%
m831
 
5.1%
l814
 
5.0%
u769
 
4.7%
S724
 
4.4%
Other values (54)5802
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1600
 
9.7%
i1387
 
8.4%
a1319
 
8.0%
e1127
 
6.9%
t1048
 
6.4%
d1022
 
6.2%
m831
 
5.1%
l814
 
5.0%
u769
 
4.7%
S724
 
4.4%
Other values (54)5802
35.3%

capacity
Real number (ℝ)

Missing  Zeros 

Distinct335
Distinct (%)41.1%
Missing21
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean16920.545
Minimum0
Maximum162000
Zeros46
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2025-12-07T22:34:58.359877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13000
median6000
Q320029.5
95-th percentile70243.75
Maximum162000
Range162000
Interquartile range (IQR)17029.5

Descriptive statistics

Standard deviation23377.212
Coefficient of variation (CV)1.3815874
Kurtosis4.0702278
Mean16920.545
Median Absolute Deviation (MAD)4000
Skewness2.0351191
Sum13807165
Variance5.4649404 × 108
MonotonicityNot monotonic
2025-12-07T22:34:58.397517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
046
 
5.5%
400040
 
4.8%
500039
 
4.7%
300036
 
4.3%
1000033
 
3.9%
350031
 
3.7%
250029
 
3.5%
200022
 
2.6%
600021
 
2.5%
450013
 
1.6%
Other values (325)506
60.5%
(Missing)21
 
2.5%
ValueCountFrequency (%)
046
5.5%
751
 
0.1%
6001
 
0.1%
7631
 
0.1%
8003
 
0.4%
100010
 
1.2%
10501
 
0.1%
11002
 
0.2%
12008
 
1.0%
12501
 
0.1%
ValueCountFrequency (%)
1620001
0.1%
1076011
0.1%
1065721
0.1%
1027801
0.1%
1027331
0.1%
1024551
0.1%
1023211
0.1%
1018211
0.1%
1001191
0.1%
1000001
0.1%

grass
Boolean

Missing 

Distinct2
Distinct (%)0.6%
Missing511
Missing (%)61.1%
Memory size27.6 KiB
False
246 
True
80 
(Missing)
511 
ValueCountFrequency (%)
False246
29.4%
True80
 
9.6%
(Missing)511
61.1%
2025-12-07T22:34:58.423863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

dome
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing3
Missing (%)0.4%
Memory size29.5 KiB
False
809 
True
 
25
(Missing)
 
3
ValueCountFrequency (%)
False809
96.7%
True25
 
3.0%
(Missing)3
 
0.4%
2025-12-07T22:34:58.443931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

city
Text

Distinct613
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Memory size47.3 KiB
2025-12-07T22:34:58.542337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length16
Mean length8.7634409
Min length3

Characters and Unicode

Total characters7335
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique468 ?
Unique (%)55.9%

Sample

1st rowPortales
2nd rowMiami
3rd rowNassau
4th rowValley City
5th rowMurfreesboro
ValueCountFrequency (%)
city16
 
1.6%
saint14
 
1.4%
new11
 
1.1%
san11
 
1.1%
atlanta7
 
0.7%
chicago7
 
0.7%
washington7
 
0.7%
houston7
 
0.7%
west7
 
0.7%
jacksonville6
 
0.6%
Other values (631)919
90.8%
2025-12-07T22:34:58.687922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e678
 
9.2%
a615
 
8.4%
n594
 
8.1%
o551
 
7.5%
i488
 
6.7%
l478
 
6.5%
r451
 
6.1%
t405
 
5.5%
s340
 
4.6%
u194
 
2.6%
Other values (43)2541
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)7335
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e678
 
9.2%
a615
 
8.4%
n594
 
8.1%
o551
 
7.5%
i488
 
6.7%
l478
 
6.5%
r451
 
6.1%
t405
 
5.5%
s340
 
4.6%
u194
 
2.6%
Other values (43)2541
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7335
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e678
 
9.2%
a615
 
8.4%
n594
 
8.1%
o551
 
7.5%
i488
 
6.7%
l478
 
6.5%
r451
 
6.1%
t405
 
5.5%
s340
 
4.6%
u194
 
2.6%
Other values (43)2541
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7335
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e678
 
9.2%
a615
 
8.4%
n594
 
8.1%
o551
 
7.5%
i488
 
6.7%
l478
 
6.5%
r451
 
6.1%
t405
 
5.5%
s340
 
4.6%
u194
 
2.6%
Other values (43)2541
34.6%

state
Text

Distinct52
Distinct (%)6.2%
Missing4
Missing (%)0.5%
Memory size41.7 KiB
2025-12-07T22:34:58.755009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0012005
Min length2

Characters and Unicode

Total characters1667
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.4%

Sample

1st rowNM
2nd rowFL
3rd rowND
4th rowTN
5th rowGA
ValueCountFrequency (%)
tx59
 
7.1%
pa57
 
6.8%
oh44
 
5.3%
nc37
 
4.4%
ny35
 
4.2%
il34
 
4.1%
ma32
 
3.8%
mn28
 
3.4%
ca27
 
3.2%
va27
 
3.2%
Other values (42)453
54.4%
2025-12-07T22:34:58.846250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A250
15.0%
N184
11.0%
M143
 
8.6%
I129
 
7.7%
C111
 
6.7%
T102
 
6.1%
O100
 
6.0%
L97
 
5.8%
X59
 
3.5%
P57
 
3.4%
Other values (15)435
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1667
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A250
15.0%
N184
11.0%
M143
 
8.6%
I129
 
7.7%
C111
 
6.7%
T102
 
6.1%
O100
 
6.0%
L97
 
5.8%
X59
 
3.5%
P57
 
3.4%
Other values (15)435
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1667
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A250
15.0%
N184
11.0%
M143
 
8.6%
I129
 
7.7%
C111
 
6.7%
T102
 
6.1%
O100
 
6.0%
L97
 
5.8%
X59
 
3.5%
P57
 
3.4%
Other values (15)435
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1667
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A250
15.0%
N184
11.0%
M143
 
8.6%
I129
 
7.7%
C111
 
6.7%
T102
 
6.1%
O100
 
6.0%
L97
 
5.8%
X59
 
3.5%
P57
 
3.4%
Other values (15)435
26.1%

zip
Text

Missing 

Distinct665
Distinct (%)91.7%
Missing112
Missing (%)13.4%
Memory size41.9 KiB
2025-12-07T22:34:58.954387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters3625
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique614 ?
Unique (%)84.7%

Sample

1st row88130
2nd row33199
3rd row11545
4th row40324
5th row29108
ValueCountFrequency (%)
016134
 
0.6%
600223
 
0.4%
558113
 
0.4%
205493
 
0.4%
560873
 
0.4%
554883
 
0.4%
303153
 
0.4%
719983
 
0.4%
274012
 
0.3%
652512
 
0.3%
Other values (656)697
96.0%
2025-12-07T22:34:59.098410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0525
14.5%
1465
12.8%
2445
12.3%
7349
9.6%
3337
9.3%
4337
9.3%
5332
9.2%
6313
8.6%
8265
7.3%
9228
6.3%
Other values (3)29
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)3625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0525
14.5%
1465
12.8%
2445
12.3%
7349
9.6%
3337
9.3%
4337
9.3%
5332
9.2%
6313
8.6%
8265
7.3%
9228
6.3%
Other values (3)29
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0525
14.5%
1465
12.8%
2445
12.3%
7349
9.6%
3337
9.3%
4337
9.3%
5332
9.2%
6313
8.6%
8265
7.3%
9228
6.3%
Other values (3)29
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0525
14.5%
1465
12.8%
2445
12.3%
7349
9.6%
3337
9.3%
4337
9.3%
5332
9.2%
6313
8.6%
8265
7.3%
9228
6.3%
Other values (3)29
 
0.8%

countryCode
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.6%
Missing3
Missing (%)0.4%
Memory size41.8 KiB
US
828 
IE
 
2
AU
 
2
BS
 
1
USA
 
1

Length

Max length3
Median length2
Mean length2.001199
Min length2

Characters and Unicode

Total characters1669
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowUS
2nd rowUS
3rd rowBS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US828
98.9%
IE2
 
0.2%
AU2
 
0.2%
BS1
 
0.1%
USA1
 
0.1%
(Missing)3
 
0.4%

Length

2025-12-07T22:34:59.139611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:34:59.160160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
us828
99.3%
ie2
 
0.2%
au2
 
0.2%
bs1
 
0.1%
usa1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
U831
49.8%
S830
49.7%
A3
 
0.2%
I2
 
0.1%
E2
 
0.1%
B1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U831
49.8%
S830
49.7%
A3
 
0.2%
I2
 
0.1%
E2
 
0.1%
B1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U831
49.8%
S830
49.7%
A3
 
0.2%
I2
 
0.1%
E2
 
0.1%
B1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U831
49.8%
S830
49.7%
A3
 
0.2%
I2
 
0.1%
E2
 
0.1%
B1
 
0.1%

timezone
Categorical

High correlation  Missing 

Distinct11
Distinct (%)3.5%
Missing523
Missing (%)62.5%
Memory size48.7 KiB
America/New_York
139 
America/Chicago
107 
America/Los_Angeles
25 
America/Denver
17 
America/Detroit
 
8
Other values (6)
18 

Length

Max length28
Median length27
Mean length16.031847
Min length13

Characters and Unicode

Total characters5034
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st rowAmerica/New_York
2nd rowAmerica/New_York
3rd rowAmerica/New_York
4th rowAmerica/Chicago
5th rowAmerica/Los_Angeles

Common Values

ValueCountFrequency (%)
America/New_York139
 
16.6%
America/Chicago107
 
12.8%
America/Los_Angeles25
 
3.0%
America/Denver17
 
2.0%
America/Detroit8
 
1.0%
America/Indiana/Indianapolis7
 
0.8%
America/Phoenix5
 
0.6%
Australia/Sydney2
 
0.2%
America/Boise2
 
0.2%
Pacific/Honolulu1
 
0.1%
(Missing)523
62.5%

Length

2025-12-07T22:34:59.189335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
america/new_york139
44.3%
america/chicago107
34.1%
america/los_angeles25
 
8.0%
america/denver17
 
5.4%
america/detroit8
 
2.5%
america/indiana/indianapolis7
 
2.2%
america/phoenix5
 
1.6%
australia/sydney2
 
0.6%
america/boise2
 
0.6%
pacific/honolulu1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e553
11.0%
r477
 
9.5%
i460
 
9.1%
a451
 
9.0%
c421
 
8.4%
A338
 
6.7%
/322
 
6.4%
m311
 
6.2%
o296
 
5.9%
_164
 
3.3%
Other values (26)1241
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)5034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e553
11.0%
r477
 
9.5%
i460
 
9.1%
a451
 
9.0%
c421
 
8.4%
A338
 
6.7%
/322
 
6.4%
m311
 
6.2%
o296
 
5.9%
_164
 
3.3%
Other values (26)1241
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e553
11.0%
r477
 
9.5%
i460
 
9.1%
a451
 
9.0%
c421
 
8.4%
A338
 
6.7%
/322
 
6.4%
m311
 
6.2%
o296
 
5.9%
_164
 
3.3%
Other values (26)1241
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e553
11.0%
r477
 
9.5%
i460
 
9.1%
a451
 
9.0%
c421
 
8.4%
A338
 
6.7%
/322
 
6.4%
m311
 
6.2%
o296
 
5.9%
_164
 
3.3%
Other values (26)1241
24.7%

latitude
Real number (ℝ)

High correlation  Missing 

Distinct742
Distinct (%)93.3%
Missing42
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean38.187433
Minimum-33.888907
Maximum53.33523
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.2%
Memory size6.7 KiB
2025-12-07T22:34:59.221381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-33.888907
5-th percentile30.076729
Q134.797225
median39.343978
Q341.852307
95-th percentile45.027096
Maximum53.33523
Range87.224137
Interquartile range (IQR)7.0550818

Descriptive statistics

Standard deviation5.9684259
Coefficient of variation (CV)0.15629293
Kurtosis52.023037
Mean38.187433
Median Absolute Deviation (MAD)3.2406995
Skewness-4.5868781
Sum30359.009
Variance35.622108
MonotonicityNot monotonic
2025-12-07T22:34:59.257631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.26256214
 
0.5%
41.87556164
 
0.5%
44.94974874
 
0.5%
46.77293223
 
0.4%
42.10187643
 
0.4%
34.12092923
 
0.4%
38.89503683
 
0.4%
37.73429622
 
0.2%
46.87390812
 
0.2%
42.50062432
 
0.2%
Other values (732)765
91.4%
(Missing)42
 
5.0%
ValueCountFrequency (%)
-33.8889071
0.1%
-33.8469911
0.1%
21.2942941
0.1%
21.37280141
0.1%
25.0555691
0.1%
25.714145551
0.1%
25.75250141
0.1%
25.7780561
0.1%
25.95796651
0.1%
26.14219761
0.1%
ValueCountFrequency (%)
53.335231
0.1%
48.36053361
0.1%
48.232511
0.1%
47.9106221
0.1%
47.77399881
0.1%
47.65719341
0.1%
47.65032351
0.1%
47.5952171
0.1%
47.492781
0.1%
47.47854181
0.1%

longitude
Real number (ℝ)

High correlation  Missing 

Distinct742
Distinct (%)93.3%
Missing42
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean-87.36896
Minimum-157.92996
Maximum151.22534
Zeros0
Zeros (%)0.0%
Negative793
Negative (%)94.7%
Memory size6.7 KiB
2025-12-07T22:34:59.299444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-157.92996
5-th percentile-117.71978
Q1-93.875237
median-85.483656
Q3-79.095068
95-th percentile-71.938034
Maximum151.22534
Range309.15531
Interquartile range (IQR)14.780169

Descriptive statistics

Standard deviation17.79547
Coefficient of variation (CV)-0.20368184
Kurtosis81.005853
Mean-87.36896
Median Absolute Deviation (MAD)7.570128
Skewness5.6426477
Sum-69458.323
Variance316.67876
MonotonicityNot monotonic
2025-12-07T22:34:59.339121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-71.80188774
 
0.5%
-87.62442124
 
0.5%
-93.09310284
 
0.5%
-92.12512183
 
0.4%
-72.58867273
 
0.4%
-93.0537843
 
0.4%
-77.03654273
 
0.4%
-79.35392922
 
0.2%
-96.75386742
 
0.2%
-90.66479852
 
0.2%
Other values (732)765
91.4%
(Missing)42
 
5.0%
ValueCountFrequency (%)
-157.92996451
0.1%
-157.8193381
0.1%
-123.28143411
0.1%
-123.22907771
0.1%
-123.1944631
0.1%
-123.11144052
0.2%
-123.06848831
0.1%
-123.0331211
0.1%
-122.9727511
0.1%
-122.906941
0.1%
ValueCountFrequency (%)
151.2253431
0.1%
151.0634351
0.1%
-6.2284471
0.1%
-68.67247181
0.1%
-68.77781381
0.1%
-68.799821
0.1%
-69.63171211
0.1%
-69.96532781
0.1%
-70.21477641
0.1%
-70.25485961
0.1%

elevation
Real number (ℝ)

Missing 

Distinct333
Distinct (%)100.0%
Missing504
Missing (%)60.2%
Infinite0
Infinite (%)0.0%
Mean249.41071
Minimum-81.603639
Maximum2200.1536
Zeros1
Zeros (%)0.1%
Negative1
Negative (%)0.1%
Memory size6.7 KiB
2025-12-07T22:34:59.376404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-81.603639
5-th percentile3.4288105
Q133.873486
median154.43466
Q3270.71487
95-th percentile1281.0218
Maximum2200.1536
Range2281.7572
Interquartile range (IQR)236.84139

Descriptive statistics

Standard deviation371.39436
Coefficient of variation (CV)1.4890875
Kurtosis9.2145387
Mean249.41071
Median Absolute Deviation (MAD)118.48904
Skewness2.969211
Sum83053.766
Variance137933.77
MonotonicityNot monotonic
2025-12-07T22:34:59.414545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.9982091
 
0.1%
3.6136288641
 
0.1%
44.972507481
 
0.1%
241.2487641
 
0.1%
19.05645371
 
0.1%
103.00281521
 
0.1%
1528.41
 
0.1%
1634.0411381
 
0.1%
230.06605531
 
0.1%
190.3655091
 
0.1%
Other values (323)323
38.6%
(Missing)504
60.2%
ValueCountFrequency (%)
-81.6036391
0.1%
01
0.1%
0.5081857441
0.1%
1.3365275861
0.1%
1.681492091
0.1%
1.7185817961
0.1%
1.8533506391
0.1%
1.964288951
0.1%
2.2642765051
0.1%
2.5746245381
0.1%
ValueCountFrequency (%)
2200.1535641
0.1%
2097.8847661
0.1%
2024.8757321
0.1%
1763.976441
0.1%
1634.0411381
0.1%
1583.5325931
0.1%
1574.9208981
0.1%
1554.4719241
0.1%
1528.41
0.1%
1507.5086671
0.1%

constructionYear
Real number (ℝ)

Missing 

Distinct100
Distinct (%)30.2%
Missing506
Missing (%)60.5%
Infinite0
Infinite (%)0.0%
Mean1968.1692
Minimum1884
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2025-12-07T22:34:59.451793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1884
5-th percentile1922
Q11939
median1969
Q31998
95-th percentile2013
Maximum2022
Range138
Interquartile range (IQR)59

Descriptive statistics

Standard deviation31.719821
Coefficient of variation (CV)0.01611641
Kurtosis-1.1057887
Mean1968.1692
Median Absolute Deviation (MAD)29
Skewness-0.19599149
Sum651464
Variance1006.147
MonotonicityNot monotonic
2025-12-07T22:34:59.588429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192410
 
1.2%
19678
 
1.0%
19988
 
1.0%
19507
 
0.8%
19667
 
0.8%
19237
 
0.8%
19687
 
0.8%
19937
 
0.8%
19696
 
0.7%
19306
 
0.7%
Other values (90)258
30.8%
(Missing)506
60.5%
ValueCountFrequency (%)
18841
 
0.1%
18951
 
0.1%
19031
 
0.1%
19121
 
0.1%
19131
 
0.1%
19142
0.2%
19153
0.4%
19171
 
0.1%
19192
0.2%
19201
 
0.1%
ValueCountFrequency (%)
20221
 
0.1%
20176
0.7%
20163
0.4%
20151
 
0.1%
20145
0.6%
20133
0.4%
20126
0.7%
20113
0.4%
20106
0.7%
20094
0.5%

Interactions

2025-12-07T22:34:57.582050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:56.658926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:56.839629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.044525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.215810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.406222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.613969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:56.690896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:56.879858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.079173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.246579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.434192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.654069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:56.723990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:56.914376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.110918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.287020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.463788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.686535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:56.752845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:56.945826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.138483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.317964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.494963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.718077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:56.784578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:56.979732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.167471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.349082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.527592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.747885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:56.810380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.010790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.190215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.376525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:57.553251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-07T22:34:59.617563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
capacityconstructionYearcountryCodedomeelevationgrassidlatitudelongitudetimezone
capacity1.000-0.2370.1620.254-0.0040.352-0.472-0.269-0.1570.049
constructionYear-0.2371.0000.2760.161-0.0480.1950.138-0.0740.0160.000
countryCode0.1620.2761.0000.0000.0000.1740.2030.6100.8150.985
dome0.2540.1610.0001.0000.1620.0110.3190.0610.1370.335
elevation-0.004-0.0480.0000.1621.0000.000-0.0030.284-0.3340.332
grass0.3520.1950.1740.0110.0001.0000.1550.1930.1420.000
id-0.4720.1380.2030.319-0.0030.1551.0000.1260.0270.142
latitude-0.269-0.0740.6100.0610.2840.1930.1261.0000.1820.591
longitude-0.1570.0160.8150.137-0.3340.1420.0270.1821.0000.929
timezone0.0490.0000.9850.3350.3320.0000.1420.5910.9291.000

Missing values

2025-12-07T22:34:57.796601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-07T22:34:57.841253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-07T22:34:57.896757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamecapacitygrassdomecitystatezipcountryCodetimezonelatitudelongitudeelevationconstructionYear
05938Al Whitehead Field at Greyhound Stadium4000.0NaNFalsePortalesNM88130USNaN34.186192-103.334397NaNNaN
1218FIU Stadium20000.0FalseFalseMiamiFL33199USAmerica/New_York25.752501-80.3778911.7185821995.0
24779Thomas A. Robinson National Stadium15000.0TrueFalseNassauNaNNaNBSNaN25.055569-77.3587363.1828891981.0
311591Lokken StadiumNaNNaNFalseValley CityNDNaNUSNaNNaNNaNNaNNaN
45220Garrison Stadium5000.0NaNFalseMurfreesboroTNNaNUSNaN36.434053-77.09843118.6911702007.0
53884RAM StadiumNaNNaNFalseEast PointGANaNUSNaNNaNNaNNaNNaN
611589Hinchliffe StadiumNaNNaNTruePatersonNJNaNUSNaNNaNNaNNaNNaN
76043Bethpage Federal Credit Union Stadium6000.0FalseFalseBrookvilleNY11545USAmerica/New_York40.816284-73.589546236.0000001966.0
811539Charlotte & Gordon Hansen StadiumNaNNaNFalseJamestownNDNaNUSNaNNaNNaNNaNNaN
911488Centreville Bank StadiumNaNTrueNaNPawtucketRINaNUSANaNNaNNaNNaNNaN
idnamecapacitygrassdomecitystatezipcountryCodetimezonelatitudelongitudeelevationconstructionYear
8273958Tiger Stadium (LA)102321.0TrueFalseBaton RougeLA70803USAmerica/Chicago30.412035-91.1838167.6407351924.0
8283825DATCU Stadium30850.0FalseFalseDentonTX76210USAmerica/Chicago33.203899-97.159245198.6871952011.0
8293836Memorial Stadium (Clemson, SC)81500.0TrueFalseClemsonSC29634USAmerica/New_York34.678774-82.843243205.6452181942.0
8303660Valley Children's Stadium41031.0FalseFalseFresnoCA93740USAmerica/Los_Angeles36.814353-119.758009102.5640641980.0
8313887Razorback Stadium80000.0FalseFalseFayettevilleAR72702USAmerica/Chicago36.068066-94.178953403.7972411938.0
8323622GEHA Field at Arrowhead Stadium76416.0TrueFalseKansas CityMO64129USAmerica/Chicago39.048889-94.483889256.7985841972.0
83310803Pen-Air FieldNaNNaNFalsePensacolaFLNaNUSNaNNaNNaNNaNNaN
83410460Betty T. Ferguson Recreational ComplexNaNTrueNaNMiami GardensFLNaNNaNNaNNaNNaNNaNNaN
8356006Robert and Janet Vackar StadiumNaNNaNFalseEdinburgTXNaNUSNaNNaNNaNNaNNaN
8363697Doak Campbell Stadium67277.0TrueFalseTallahasseeFL32306USAmerica/New_York30.438169-84.30440319.5594251950.0